The article is devoted to the application of large language models (BMS) in information tasks of decision support systems using the example of healthcare. The key BAYAM architectures and their practical implementations are considered, as well as the capabilities of these models for natural language processing and medical data analysis. Special attention is paid to the role of BAM in automating decision-making processes, including optimizing access to knowledge from clinical recommendations. Examples of the use of BYAM in various fields of medicine are presented. In addition, the prospects for further development of BYAM in healthcare and related challenges are discussed.

Keywords: big language models, natural language processing, decision support systems (DSS), industrial engineering, clinical guidelines, international classification of diseases

The article shows the importance of the role of clinical engineering departments in ensuring high-quality comprehensive control of the state of medical equipment at all stages of its life cycle. The main resource of such departments is the resource of working time of clinical engineers. In order to rationalize the use of this resource, we analyzed data on the time budget for the maintenance of 2459 units of medical equipment in medical institutions of Volgograd. The results of the analysis allowed us to identify 3 subgroups of medical equipment in the general data array that have statistically significant differences in the time characteristics of their maintenance and require a different approach to managing the time of clinical engineers servicing them. At the final stage of this study, ABC-XYZ analysis was used. Its results allowed us to formulate recommendations for rationalizing the use of working time of clinical engineers to work with the subgroups of medical equipment identified in this study in practical conditions.

Keywords: clinical engineering, clinical engineer, time budget, maintenance of medical equipment